دانشگاه کاشان مهندسی اکوسیستم بیابان 2538-6336 13 45 2025 04 21 Relationship between habitat quality and amount of exploitation of Ferula assafoetida to spatially estimate its economic value In South Khorasan Province ارتباط کیفیت عرصۀ رویشگاهی و مقدار بهره‌‌برداری آنغوزه شیرین جهت تخمین مکانی ارزش اقتصادی آن در استان خراسان جنوبی 1 14 114781 ‎10.22052/deej.2025.255307.1070 FA فاطمه جهانی شکیب گروه محیط زیست، دانشکده منابع و محیط زیست، دانشگاه بیرجند محمد ساغری دانشکده منابع طبیعی و محیط زیست، دانشگاه بیرجند طاهره اردکانی استادیار گروه علوم و مهندسی محیط‌زیست، دانشکده کشاورزی و منابع طبیعی، دانشگاه اردکان، اردکان، ایران Journal Article 2024 09 29 Introduction Quantifying ecosystem services within habitats provides a relative understanding of an ecosystem's status. However, for effective development, proper utilization, and informed decision-making, the spatial valuation of these services is essential after they have been quantified. Understanding the economic value of ecosystem services and biodiversity is crucial for several reasons. Notably, the persuasive power of economic language, specifically the monetary value that nature provides, serves as a powerful tool to communicate the importance of conservation to a broader, and often skeptical, audience. Consequently, when ecosystem service management becomes an institutional priority, various policies can be implemented to influence human interaction with the environment and to promote sustainable conservation planning of these valuable resources. This study aims to estimate the spatial economic value by quantifying habitat service quality in South Khorasan Province using the InVEST model. Materials and Methods This research focuses on the rangelands of South Khorasan Province. Specifically, the primary habitats of Ferula assafoetida are located in the Tabas and Boshruyeh counties. Initially, habitat quality was modeled using the InVEST method. This model is capable of assessing habitats based on human threats, the relative weight of each threat, land use, habitat sensitivity to threats, distance from habitat to threat sources, and habitat presence or absence. Subsequently, using reliable data on Ferula assafoetida harvesting in these rangelands, the relationship between habitat quality and plant yield was examined. This relationship was analyzed through spatial statistics, comparing habitat quality maps with the average yield in productive rangeland areas of Ferula assafoetida within the study area. The findings were then extrapolated to the entire rangeland area of Ferula assafoetida within the region, based on the derived regression equation. Finally, yield was estimated based on habitat quality and the corresponding spatial regression equation. In the final stage, using the pixel-level yield estimates and the market price of Ferula assafoetida products, the economic value of the rangeland areas was calculated. Results The threats identified in the habitat quality modeling included those arising from transportation (major roads, secondary roads, and railways), industry and mining, settlements, agriculture, dust sources, and grazing areas. Based on spatial statistical analysis, a spatial regression equation (Y = 0.000003 + 0.008542X) was derived, relating habitat quality maps to yield in productive areas. This equation was then extrapolated to the entire Ferula assafoetida rangeland area within the study region, and the estimated yield was mapped accordingly. Subsequently, the economic value of the Ferula assafoetida rangelands was determined based on the yield map and market prices, resulting in a pixel-level map illustrating the spatial distribution of economic value in millions of Rials. Spatial statistics indicated that the highest economic value within the studied pixels (900 square meters, equivalent to 0.09 hectares) was 0.085 million Rials, or 850,000 Rials. The mean and variance of the economic value in the rangeland areas were estimated to be 0.042 million Rials and 0.026 million Rials, respectively. The total economic value was estimated at 443,466.1 million Rials, approximately 0.44 trillion Rials. Discussion and conclusion The habitat service quality distribution map for South Khorasan Province, based on the habitat quality index, revealed that the majority of high-quality areas are concentrated in the northeastern part of the province. This concentration is attributed, in part, to the more favorable climate and higher average rainfall in these regions. Similar conditions are observed in the western-central part of the province, albeit these areas are situated within the desert regions of South Khorasan. Methodologically, this study diverges from other research, such as Sekouti Eskooie (2014), which utilized the soil quality index to evaluate rangeland production potential. While rangeland production potential represents another form of ecosystem service, this study focused on assessing the habitat quality index by considering habitat threat factors and sources, and habitat ecosystem services. Regarding the economic valuation of medicinal plants, numerous studies have been conducted, often overlooking the spatial aspect of habitat value. For example, Khosravi and Mehrabi (2005) performed an economic analysis of Ferula assafoetida harvesting in the Tabas region. Over a four-year harvesting period across seven Ferula assafoetida habitats in Tabas County, they reported a total revenue of approximately 6,030.5 million Rials, with a production yield of 96,440 kilograms. Although direct comparison between their findings and this study is challenging due to temporal and inflationary differences, the non-spatial and quantitative nature of their reported figures is evident. Employing such assessments provides policymakers with valuable information to balance financial benefits with the preservation of diverse plant species, especially in areas prone to land degradation. The innovative and practical approach of this study offers land managers a rapid understanding of the area, considering cost, time, and data volume constraints. A key advantage of this research is its potential to justify and promote the sustainable utilization of medicinal plants. This is crucial because economic development, job creation, and export promotion depend on awareness of ecosystem status and the value of medicinal plant habitats, which this study addresses. ارتباط کیفیت عرصۀ رویشگاهی و مقدار بهره‌‌برداری آنغوزه شیرین جهت تخمین مکانی ارزش اقتصادی آن در استان خراسان جنوبی

https://deej.kashanu.ac.ir/article_114781_3480423ed7835e2b15cd08fc8d5e7b77.pdf

دانشگاه کاشان مهندسی اکوسیستم بیابان 2538-6336 13 45 2025 04 21 Modelling of sodium absorption ratio (SAR) using some of the Artificial Intelligent Models (AIM) (Case study: Some of the hydrometric stations of Kashkan watershed) مدل‌سازی نسبت جذب سدیم با استفاده از برخی مدل‌های هوش مصنوعی (مطالعۀ موردی: برخی ایستگاه‌های هیدرومتری حوزۀ آبخیز کشکان) 15 32 114801 ‎10.22052/deej.2025.255227.1066 FA علیرضا سپه وند دانشیار گروه مهندسی مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه لرستان، خرم‌آباد، لرستان، ایران نسرین بیرانوند دانشجوی دکتری، گروه مهندسی مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه لرستان، لرستان، خرم‌آباد، ایران رضا فتحی گنجی دانشجوی دکتری، گروه مهندسی مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه لرستان، خرم‌آباد، لرستان، ایران Journal Article 2024 08 15 Introduction: Water quality plays a crucial role in the optimal management of water resources, directly impacting both public health and the environment. Water finds its application across various sectors, including agriculture, drinking water supply, and industry. Among the diverse sources of water, rivers have been historically favored for the development of human societies due to their accessibility. Within river systems, the Sodium Adsorption Ratio (SAR) stands out as a key water quality parameter used to assess the suitability of water for both drinking and agricultural purposes. The ratio of sodium ions to calcium and magnesium ions serves as a predictor of the extent to which irrigation water tends to engage in cation-exchange reactions within the soil. This ratio, termed SAR (SAR=), is instrumental in determining the sodium hazard associated with irrigation waters. Given the significant role of the Sodium Adsorption Ratio (SAR) in soil management and stability, its accurate estimation holds particular importance. The purpose of this study was to model the Sodium Adsorption Ratio (SAR) using selected Artificial Intelligent Models (AIM). Materials and methods: The study area encompasses a portion of the Karkheh watershed, situated in the central Zagros Mountains within Lorestan province, Iran. 1 This watershed was selected as an appropriate case study for evaluating Sodium Absorption Ratio Modeling (SARM). Geographically, the study area lies between 47°12′30″ to 48°59′20″ East longitudes and 33°05′45″ to 34°03′26″ North latitudes, covering an approximate area of 8844.6 km². The watershed's elevation ranges from 760 to 3646 meters above sea level. This region is classified as semi-arid, with mean annual rainfall varying according to topography and location, exhibiting a significant spatial variation from 401 mm in the lower valley to 473 mm in the upper watershed. Consequently, this study compared the performance of three soft computing techniques—Artificial Neural Network-Multi Layer Perceptron (ANN-MLP), Linear Regression (LR), and Random Forest (RF)—to estimate the Sodium Absorption Ratio (SAR) at the Chamanjir, Doab Visian, Cholhol Afrineh, Kashkan Afrineh, and Kashkan Poldokhtar hydrometry stations within the Karkheh watershed, Lorestan province, Iran. The dataset comprised observational water quality data (for training and testing) from the Kashkan watershed in Iran, spanning the period from 1968 to 2023. The complete dataset included measurements of Total Dissolved Solids (TDS), Electrical Conductivity (EC), pH, Bicarbonate (HCO-), Chloride (Cl), Sulfate (SO-), Calcium (Ca), Magnesium (Mg), Sodium (Na), and SAR from the five aforementioned hydrometric stations. Of this data, 70% was used to train the models, while the remaining 30% was used for model testing. Finally, the accuracy of the models was evaluated using three performance metrics: Correlation Coefficient (C.C.), Maximum Absolute Error (MAE), and Root Mean Square Error (RMSE). Result: The findings of this study indicate that the Artificial Neural Network-Multi Layer Perceptron (ANN-MLP) model demonstrates superior accuracy in estimating the Sodium Absorption Ratio (SAR) compared to the Random Forest (RF) and Linear Regression (LR) models for the specified study area. Specifically, the test results for the MLP model revealed the following performance metrics: at the Chamanjir station, the Correlation Coefficient (C.C.) was 0.99, the Maximum Absolute Error (MAE) was 0.03, and the Root Mean Square Error (RMSE) was 0.05; at the Cholhol Afrineh station, these values were 0.93, 0.09, and 0.18, respectively; for the Doab Visian station, the results were 0.99, 0.01, and 0.02; at the Kashkan Afrineh station, they were 0.87, 0.10, and 0.24; and finally, at the Kashkan Poldokhtar station, the values were 0.92, 0.05, and 0.19. Furthermore, sensitivity analysis revealed that Sodium (Na) is the most influential parameter in the estimation/prediction of the Sodium Absorption Ratio (SAR) across all the examined hydrometry stations. Discussion and Conclusion: The application of soft computing techniques in predicting Water Quality Indices (WQI) can streamline the process and reduce the time involved. In this study, three such techniques—Random Forest (RF), Artificial Neural Network-Multi Layer Perceptron (ANN-MLP), and Linear Regression (LR)—were employed for the prediction of the Sodium Absorption Ratio (SAR) within the Kashkan watershed in Lorestan province, Iran. The results demonstrated that by sampling and measuring various hydro-chemical parameters and subsequently applying soft computing techniques, SAR can be predicted with a high degree of accuracy. Based on the findings of this research, these optimized models offer a viable alternative to the often costly and time-consuming traditional methods of estimating the Sodium Absorption Ratio (SAR) in rivers. Furthermore, these models hold potential for estimating the Sodium Absorption Ratio (SAR) in nearby rivers, even in the absence of hydrometry stations, thereby providing valuable tools for the effective management of surface water quality. با توجه به اهمیت جذب سدیم در مدیریت و پایداری خاک و همچنین کاربرد این نسبت در آب‌های سطحی، هدف این تحقیق، مقایسۀ عملکرد و کارایی مدل‌های ANN-MLP، LR و RF در تخمین و برآورد نسبت جذب سدیم در ایستگاه‌های هیدرومتری چم‌انجیر، دوآب ویسیان، چولهول افرینه، کشکان افرینه و کشکان پلدختر در استان لرستان است. پارامترهای ورودی و خروجی به‌منظور تخمین و مدل‌سازی نسبت جذب سدیم در این تحقیق، شامل TDS، EC، pH، HCO3، CL، SO4، Ca، Mg و Na بوده است که 70 درصد داده‌ها برای مرحلۀ آموزش و 30 درصد باقی‌مانده در مرحلۀ آزمایش مدل‌سازی استفاده شد. برای مقایسۀ کارایی مدل‌ها از معیارهای سنجش خطای ارزیابی ضریب همبستگی (CC)، میانگین خطای مطلق (MAE) و ریشۀ میانگین مربعات خطا (RMSE) استفاده شد. نتایج این تحقیق نشان داد که مدل MLP برای تخمین SAR در مقایسه با سایر مدل‌های به‌کار برده‌شده در این تحقیق، از کارایی بالاتری برای تخمین نسبت جذب سدیم برخوردار بوده است. نتایج معیارهای سنجش خطا برای مدل MLP به‌ترتیب گفته‌شده در ایستگاه چم‌انجیر برابر 99/0، 03/0 و 05/0، ایستگاه چولهول افرینه برابر 93/0، 09/0 و 18/0، ایستگاه دوآب ویسیان برابر 99/0، 01/0 و 02/0، ایستگاه کشکان افرینه برابر 87/0، 10/0 و 24/0 و ایستگاه کشکان پلدختر نیز به‌ همین ترتیب برابر 92/0، 05/0 و 19/0 و در مدل‌سازی داده‌های همۀ ایستگاه‌ها نتایج بخش آزمایش به‌ همین ترتیب برابر 96/0، 07/0 و 13/0 به‌ دست آمده است. همچنین نتایج تحلیل حساسیت مدل برتر در همۀ ایستگاه‌های مورد بررسی نشان داد که پارامتر Na بین پارامترهای ورودی این تحقیق حساس‌ترین پارامتر در مدل‌سازی نسبت جذب سدیم با استفاده از مدل MLP بوده است.

https://deej.kashanu.ac.ir/article_114801_a55cdb5cb064bd5c3c27dbf1e07c049d.pdf

دانشگاه کاشان مهندسی اکوسیستم بیابان 2538-6336 13 45 2025 04 21 Investigating the relationship between the spatial and temporal distribution of meteorological and hydrological drought indicators (Case study: Jiroft Plain) بررسی ارتباط بین توزیع مکانی و زمانی شاخص‌های خشکسالی هواشناسی و هیدرولوژیک (مطالعۀ موردی: دشت جیرفت) 33 48 114803 ‎10.22052/deej.2025.255412.1076 FA زهرا سنجری دانش آموخته کارشناسی ارشد، دانشکده کشاورزی و منابع طبیعی، دانشگاه اردکان، اردکان، ایران مهدی حیات زاده استادیار، دانشکده کشاورزی و منابع طبیعی، دانشگاه اردکان، اردکان، ایران فرزانه فتوحی فیروزآباد استادیار، دانشکده کشاورزی و منابع طبیعی، دانشگاه اردکان، اردکان، ایران 0000-0001-7162-2681 علی فتح زاده دانشیار، دانشکده کشاورزی و منابع طبیعی، دانشگاه اردکان، اردکان، ایران فاطمه روستایی صدرآباد استادیار، دانشکده کشاورزی و منابع طبیعی، دانشگاه اردکان، اردکان، ایران Journal Article 2024 09 24 Introduction: Drought is an unavoidable natural disaster with far-reaching detrimental effects on various sectors, including water resources, agriculture, and the environment. Effective drought management necessitates identifying the dominant climatic factors that contribute to these events. Consequently, employing weather indicators and models, alongside analyzing the distribution of variables to accurately understand the underlying processes, is essential. Given that over two-thirds of Iran's regions are classified as arid and semi-arid – a condition partly attributed to the Alborz and Zagros mountain ranges hindering rain clouds from reaching the central and eastern parts – and experiencing greater precipitation variability, the central and southern regions of the country face more significant environmental damage. This research was conducted to investigate the spatial and temporal distribution of meteorological (SPI) and hydrological (SDI) drought indices. Materials and Methods: In this research, precipitation data from 10 synoptic and climatology stations (covering the common statistical period of 1996-2020) and 7 hydrometric stations (covering the statistical period of 1993-2016) were utilized. Geostatistical methods were employed to analyze the spatial distribution of drought indicators, while the Mann-Kendall test was used to assess the temporal distribution and identify trends in changes. Pearson's correlation coefficient was calculated to examine the relationship between the SPI and SDI indices. The Mann-Kendall test, a non-parametric method, requires no specific distributional assumptions, making it suitable for time series that do not follow a particular distribution. The null hypothesis of this test posits randomness and the absence of a trend in the data series. Rejection of the null hypothesis indicates the presence of a statistically significant trend. Trend detection, as well as the identification of abrupt changes in the data, can be achieved through the Mann-Kendall test using both the test statistic (T) and the Mann-Kendall diagram. By inputting discharge and precipitation data into the software, the trend (or lack thereof) and any sudden shifts within the desired statistical period can be visualized using the corresponding graphs for each parameter. Results: The results of the SPI index revealed that very severe droughts occurred in some years over both short-term and long-term periods (spanning 8 consecutive years). According to the SPI classification, precipitation amounts during these years were significantly below normal. Conversely, the SDI index results for the hydrometric stations indicated below-normal flow and prevalent dry conditions in the 2000s (specifically the Iranian calendar decade of the 1380s), particularly in August, December, and March. The Kahnag-Shibani, Kenaroieh, Zarin, Dehroud, and Qhala-Rigi stations showed consistency between the meteorological and hydrological indicators, suggesting that in most years with below-normal rainfall, river flow was negatively impacted, leading to reduced flow and a downward trend. Pearson's correlation coefficient results demonstrated the strongest correlation between the SPI and SDI indices at 12, 24, and 48-month time scales. Discussion and Conclusion: Across all stations, no statistically significant relationship was found between the meteorological and hydrological drought indices. In other words, instances of hydrological drought did not consistently coincide with meteorological drought conditions. One potential reason for this discrepancy could be the influence of upstream flows contributing to baseflow at the hydrometric stations. Within the studied area, the most significant fluctuations in flow rate occurred during March, December, and August, while April, May, July, September, and November exhibited relatively stable and good flow conditions. A primary factor contributing to short-term flow variations is the utilization of moving averages and changes in precipitation across different months in the calculation of the SDI index. The analysis of SPI and SDI index variations revealed significant differences between the existing stations at the 95% and 99% confidence levels. Overall, it can be concluded that changes in river flow are influenced by the rainfall conditions in the region, albeit with a time lag. برای مقابلۀ مؤثر با خشکسالی، شناسایی عوامل اقلیمی غالب که منجر به رویدادهای خشکسالی می‌شود، بسیار بااهمیت است. تحقیق حاضر با هدف بررسی توزیع مکانی و زمانی شاخص‌های خشکسالی هواشناسی (SPI) و هیدرولوژیک (SDI) انجام گرفت. بدین منظور از اطلاعات بارش 10 ایستگاه سینوپتیک و کلیماتولوژی در دورۀ آماری مشترک (1375-1399) و 7 ایستگاه هیدرومتری در دورۀ آماری (1372-1395) استفاده شد. برای بررسی توزیع مکانی شاخص‌های خشکسالی از روش‌های زمین‌آماری و برای بررسی توزیع زمانی و تعیین روند تغییرات از آزمون من-کندال استفاده شد. برای بررسی ارتباط بین شاخص‌های SPI و SDI از ضریب همبستگی پیرسون استفاده شد. نتایج حاصل از شاخص SPI نشان داد که در دورۀ کوتاه‌مدت و بلندمدت در برخی از سال‌ها (8 سال ناپیوسته) خشکسالی خیلی شدید اتفاق افتاده است که براساس طبقه‌بندی شاخص SPI مقدار بارش در این سال‌ها خیلی کمتر از نرمال بوده است. درمقابل، نتایج شاخص SDI برای ایستگاه‌های هیدرومتری نشان داد که در دهۀ 1380 و به‌ویژه ماه‌های مرداد، آذر و اسفند جریانی کمتر از نرمال داشته و شرایط خشکی حاکم بوده است. نتایج حاصل از ضریب همبستگی پیرسون نشان داد که بیشترین همبستگی بین شاخص‌های SPI و SDI در مقیاس‌های زمانی 12، 24 و 48 ماهه وجود دارد. در همۀ ایستگاه‌ها ارتباط معنی‌داری بین شاخص خشکسالی هواشناسی و هیدرولوژیکی وجود ندارد. یکی از دلایل این اختلاف می‌تواند حاصل منشأ گرفتن جریان زیرسطحی در بالادست ایستگاه‌های هیدرومتری که باعث ایجاد جریانی پایه شده است باشد. نتایج روند تغییرات شاخص‌های SPI و SDI نشان داد که ایستگاه‌های موجود در دو سطح اطمینان 95% و 99% دارای اختلاف معناداری با یکدیگرند. به‌طور کلی می‌توان نتیجه گرفت که تغییرات جریان رودخانه پس از یک تأخیر زمانی از شرایط بارش در منطقه تأثیر می‌پذیرد.

https://deej.kashanu.ac.ir/article_114803_2fd6a9b84de11b05ac8f06e069f17cdf.pdf

دانشگاه کاشان مهندسی اکوسیستم بیابان 2538-6336 13 45 2025 04 21 A Comparative Analysis of the Performance of Deep Learning Models and Convolutional Neural Networks for Dust Storm Modeling (Case Study: Kermanshah Province) بررسی مقایسۀ عملکرد مدل‌های یادگیری عمیق و شبکۀ‌ عصبی کانولوشن به‌منظور مدل‌سازی طوفان‌های گردوغبار (مطالعۀ موردی: استان کرمانشاه) 49 62 114854 ‎10.22052/deej.2025.256139.1091 FA محمد انصاری قوجقار استادیار، گروه مهندسی احیاء مناطق خشک و کوهستانی، دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران. Journal Article 2025 01 09 Introduction: Understanding the factors contributing to the occurrence of dust storms, along with awareness of their timing and location, is crucial for managing and mitigating the damage they cause. However, limitations such as scarce resources, high costs, and the time needed for monitoring and analysis often impede effective management. Consequently, the application of deep learning models, artificial intelligence algorithms, and neural networks represents a significant advancement towards the forecasting and integrated management of this destructive climatic phenomenon. This study investigates the results of dust storm modeling using deep learning models and convolutional neural networks across nine synoptic meteorological stations in Kermanshah Province (Qasr-e Shirin, Gilan-e Gharb, Sarpol-e Zahab, Eslamabad-e Gharb, Javanrud, Sararud, Ravansar, Harsin, and Kangavar) over a 40-year statistical period (1981–2020). In this context, two computational models—deep learning and convolutional neural networks—were developed and their performance in predicting the FDSD index was compared. While most studies have focused on modeling the frequency index of dust storms using machine learning models, deep learning approaches have been less commonly utilized. Convolutional Neural Networks, typically employed for image processing and modeling, have been adapted in this research to model dust storms. Furthermore, their performance has been benchmarked against a deep learning model. Accordingly, this research aims to address existing challenges and gaps by examining the performance of deep learning models and Convolutional Neural Networks in dust storm modeling. Materials and Methods: This study investigates the performance of deep learning models and convolutional neural networks in modeling dust storms across nine meteorological stations in Kermanshah Province (Qasr-e Shirin, Gilan-e Gharb, Sarpol-e Zahab, Eslamabad-e Gharb, Javanrud, Sararud, Ravansar, Harsin, and Kangavar) over a 40-year statistical period (1981–2020). For this purpose, hourly horizontal visibility data and the World Meteorological Organization (WMO) present weather codes were utilized. Meteorological phenomena observations are recorded every three hours, totaling eight synoptic reports per day. In these observations, visual weather phenomena are defined according to the WMO's guidelines using 100 codes ranging from 00 to 99. From these 100 codes, 11 specific codes are commonly used to record and report dust storm events at various meteorological stations. Following the WMO definition, a dust storm day is defined as a day when at least one of the eight synoptic reports includes one of the dust-related codes (06, 07, 08, 09, 30, 31, 32, 33, 34, 35, or 98) in the present weather report, provided that the corresponding horizontal visibility is recorded as less than 1000 meters. In this study, a horizontal visibility of less than 1000 meters was consistently used as the criterion for identifying dust storms for all dust-related codes. This approach ensures the accurate detection and classification of dust storm events throughout the study period. Results and Discussion: The evaluation metrics R (Pearson correlation coefficient), RMSE (Root Mean Square Error), NS (Nash-Sutcliffe efficiency coefficient), and MAE (Mean Absolute Error) were used to assess and compare the performance of the deep learning model and the convolutional neural network algorithm for dust storm modeling in Kermanshah Province over a 40-year statistical period (1980–2020). The findings from dust storm modeling indicate that at Qasr-e Shirin station, which records the highest seasonal frequency of dust storm days, and Kangavar station, characterized by the lowest seasonal frequency, a clear trend emerges: as the frequency of dusty days’ decreases across the studied stations—from Qasr-e Shirin to Kangavar—the accuracy of FDSD index modeling significantly improves. The results reveal statistically significant differences between the models at the 95% and 99% confidence levels. While substantial discrepancies are observed between the outputs of the deep learning (DL) model and the convolutional neural network (CNN), the DL model proves to be the more reliable choice for achieving higher accuracy and enhancing modeling efficiency. Additionally, a t-test comparison of the observed and predicted mean values supports the null hypothesis, validating the equivalence of the observed and predicted time series means for the frequency of dust storm days in Kermanshah Province. Conclusion: Deep learning models, such as deep artificial neural networks, stand out from other methods due to their superior ability to identify patterns and hidden structures within large datasets. In these networks, each layer learns a concept that is refined and expanded upon in subsequent layers, progressively transforming basic concepts into more complex abstract ones. This study aimed to compare the performance of deep learning models and convolutional neural networks for modeling dust storms at nine meteorological stations in Kermanshah Province (Qasr-e Shirin, Gilan-e Gharb, Sarpol-e Zahab, Eslamabad-e Gharb, Javanrud, Sararud, Ravansar, Harsin, and Kangavar) over a 40-year statistical period. The results indicated that the first seasonal combination was selected as the optimal configuration for predicting the FDSD index using both deep learning and CNN models. Both models demonstrated acceptable accuracy and performance in simulating the frequency of dust storm days. However, the DL model, exhibiting higher correlation coefficients and NS values, along with lower RMSE and MAE error estimation metrics, was identified as the optimal model for dust storm modeling in Kermanshah Province. This advanced technology enables more accurate predictions of the timing, intensity, and path of dust storms, playing a fundamental role in reducing human, economic, and environmental damages. Furthermore, the results from these models can serve as an efficient tool in natural resource management—including water, soil, and vegetation—to mitigate the destructive impacts of storms and improve regional planning. شناخت عوامل مؤثر بر وقوع طوفان‌های گردوغبار و آگاهی از زمان و مکان وقوع این طوفان‌ها، نقش بسزایی در مدیریت و کاهش خسارات ناشی از آن‌ها دارد؛ اما اغلب، محدودیت‌هایی مانند کمبود منابع، هزینه‌های زیاد و صرف زمان زیاد پایش و بررسی مانع از مدیریت صحیح می‌شود. لذا استفاده از مدل‌های یادگیری عمیق، الگوریتم‌های هوش مصنوعی و شبکه‌های عصبی، گام مهمی در راستای پیش‌بینی و مدیریت یکپارچۀ این‌ پدیدۀ اقلیمی مخرب به‌ شمار می‌رود. بدین‌ترتیب، این پژوهش به بررسی نتایج مدل‌سازی طوفان‌های گردوغبار با استفاده از مدل‌های یادگیری عمیق و شبکۀ‌ عصبی کانولوشن در نه ایستگاه هواشناسی سینوپتیک استان کرمانشاه شامل قصر شیرین، گیلان غرب، سرپل ذهاب، اسلام‌آباد غرب، جوانرود، سرارود، روانسر، هرسین و کنگاور، در طول دورۀ ‌آماری 40 ساله پرداخته است. در این راستا، دو مدل محاسباتی یادگیری عمیق و شبکۀ‌ عصبی کانولوشن به‌منظور پیش‌بینی شاخص FDSD توسعه یافته و مورد مقایسه قرار گرفتند. نتایج حاکی از وجود تفاوت قابل ملاحظه‌ای در دقت روش‌های مورد بررسی بود. مدل DL با کمترین مقدار معیارهای خطای MAE و RMSE، عملکرد بهتری را نسبت به مدل CNN نشان داد؛ به‌طوری‌که ضرایب نش‌-ساتکلیف و همبستگی آن به‌ترتیب از 939/0 و 928/0 تا 971/0 و 953/0 متغیر بود. هر دو مدل بهترین عملکرد خود را در گام‌های اول و دوم نشان دادند. ایستگاه قصر شیرین با بیشترین مقدار متوسط روزهای همراه با طوفان‌های گردوغبار، بیشترین دقت را نشان داد. نتایج این مطالعه می‌تواند نقش مهمی درزمینۀ مدل‌سازی طوفان‌های گردوغبار و اتخاذ تصمیم‌های مدیریتی لازم به‌منظور کاهش خسارات این پدیده داشته باشد.

https://deej.kashanu.ac.ir/article_114854_dbe909bb21ecfdaa6fbdaf54039876aa.pdf

دانشگاه کاشان مهندسی اکوسیستم بیابان 2538-6336 13 45 2025 04 21 Effects of Biological Soil Crusts on Some Soil Physico-Chemical Propertices (Case Study: Hilslopes of Agi-Gol Wetland, Golestan Province) تأثیر پوسته‌های زیستی بر برخی از خصوصیات فیزیکی و شیمیایی خاک (مطالعۀ موردی: تپه‌های مشرف بر تالاب آجی‌گل در استان گلستان) 63 76 114855 ‎10.22052/deej.2025.255397.1074 FA راشین محمدی گروه مدیریت مناطق بیابانی، دانشکده مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران محسن حسینعلی زاده گروه مدیریت مناطق بیابانی، دانشکده مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران علی محمدیان بهبهانی دانشگاه علوم کشاورزی و منابع طبیعی گرگان نرگس کریمی نژاد عضو هیئت علمی بخش مهندسی منابع طبیعی و محیط زیست، دانشکده کشاورزی، دانشگاه شیراز، شیراز، فارس، ایران واحدبردی شیخ گروه آبخیزداری، دانشکده مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران جهانگیر محمدی گروه جنگل، دانشگاه علوم کشاورزی و منابع طبیعی گرگان Journal Article 2024 11 09 Introduction: The expansion of deserts in Iran poses a significant challenge, with 75% of the population in arid and semi-arid regions facing issues related to desertification. Within this ecosystem, biotic and abiotic environmental components and their interactions are of paramount importance. The soil biota in these areas plays a crucial role in improving soil properties and restoring degraded lands. Therefore, biological soil crusts (BSCs), as communities of living organisms on the uppermost few millimeters of the soil surface in dryland regions, perform various invaluable ecological functions and are highly susceptible to erosion. They can enhance soil physico-chemical characteristics, aid in natural resource management, and play an essential role in protecting wetlands, as well as in soil and water conservation. Hence, this study aims to assess the impact of biocrusts on the soil properties of the Ajigol wetland in the Inche Burun region of Gonbad Kavous, North of Golestan province, a hotspot of wind erosion during warm days and months north of Gorgan. Materials and Methods: In this research, the physicochemical properties of both topsoil and subsurface samples were measured, including soil organic carbon (SOC), electrical conductivity (EC), pH, sodium adsorption ratio (SAR), exchangeable sodium percentage (ESP), and the concentrations of Sodium (Na), Calcium (Ca), Potassium (K), and Magnesium (Mg). Additionally, soil particle size distribution (SPSD) was determined using laser diffraction analysis. Finally, data analysis was conducted to investigate the impact of biological soil crusts on the physical and chemical properties of the soil using the t-test and Wilcoxon test within the R programming environment. Here's an edited English version of the Results section: Results: The analysis of soil chemical properties revealed notable differences between the two depths examined: 0-1 cm and 1-5 cm. The electrical conductivity (EC) measured 199.87 dS/m at 0-1 cm and 194.51 dS/m at 1-5 cm. Potassium (K) content was higher in the surface layer, recorded at 78.29 ppm compared to 71.48 ppm in the subsurface layer. Magnesium (Mg) levels also showed a similar trend, with 1.75 ppm at 0-1 cm and 1.12 ppm at 1-5 cm. The soil organic carbon (SOC) content was significantly higher at 1.58% for the 0-1 cm depth, compared to 0.77% at 1-5 cm. However, pH levels were slightly elevated in the deeper layer, measuring 7.41 at 0-1 cm and 7.48 at 1-5 cm. Sodium (Na) content increased with depth, with values of 132.73 ppm at 0-1 cm and 137.63 ppm at 1-5 cm. Calcium (Ca) levels were comparable at 3.42 ppm for the surface layer and 3.5 ppm for the subsurface layer. The sodium adsorption ratio (SAR) showed higher values at greater depth, with 83.67 ((mmol/L)0.5) at 0-1 cm and 96.61 ((mmol/L)0.5) at 1-5 cm. Similarly, exchangeable sodium percentage (ESP) values were also higher, recorded at 128.38 ((mmol/L)0.5) at the surface layer and 142.65 ((mmol/L)0.5) at the subsurface layer. The findings indicated that biological soil crusts in the studied area significantly influenced organic carbon levels at both depths, leading to an increase in organic matter. Furthermore, potassium and magnesium concentrations were greater in the surface layer of the biological soil crusts, while sodium, calcium, SAR, and ESP concentrations increased with soil depth. For all study depths, the predominant soil particle size was silt (2-50 microns), but in the subsurface layer, the particle size was generally larger than in the surface layer. Discussion and Conclusion: Analysis of the soil's physicochemical properties at different depths did not reveal statistically significant differences across the various parameters studied. However, it was observed that biological soil crusts have a positive impact on increasing organic matter in the topsoil, with a subsequent decrease at greater depths. Soil particle size distribution was also influenced by the presence of BSCs. In general, biological soil crusts can directly and indirectly affect the physicochemical properties of the soil and play a significant role in sediment processes and the distribution of dust particles in the environment. These communities can stabilize the soil by increasing organic matter and overall soil stability, thereby contributing to soil conservation and erosion prevention. This research enhances our understanding of soil properties and sediment processes influenced by biological soil crusts in the Ajigol wetland area in Golestan province, highlighting the ecological value of this wetland ecosystem. Raising awareness among local residents regarding the importance of BSCs is crucial, and their degradation due to various factors, particularly overgrazing, should be carefully considered and mitigated. گسترش روزافزون بیابان و فرایند بیابان‌زایی در ایران پدیده‌ای چالش‌برانگیز است که سبب شده 75 درصد از بوم‌سازگان مناطق خشک و نیمه‌خشک با مشکل مواجه شوند. جمعیت‌های زیستی خاک این مناطق نقش مهمی در بهبود خواص خاک و بازسازی سرزمین‌های تخریب‌شده ایفا می‌کنند. یکی از جوامع زیستی بسیار مهم این بوم‌سازگان‌ها، پوسته‌های زیستی هستند. لذا این تحقیق با هدف بررسی تأثیر استقرار پوسته‌های زیستی خاک موجود روی تپه‌های مشرف به تالاب بین‌المللی آجی‌گل بر خصوصیات فیزیکی- شیمیایی خاک انجام شد. در این پژوهش برخی از خصوصیات ﻓﯿﺰیکوﺷﯿﻤﯿﺎیی خاک در دو عمق (0-1 و 1-5 سانتی‌متر) شامل کربن آلی، هدایت الکتریکی، اسیدیته، SAR، ESP و مقدار سدیم، کلسیم، پتاسیم و منیزیم اندازه‌گیری شدند. نتایج نشان داد که پوسته‌های زیستی تنها درخصوص کربن آلی تفاوت معنی‌داری در دو عمق خاک مورد مطالعه ایجاد نموده است. افزایش مادۀ آلی در خاک به‌دلیل وجود پوسته‌های زیستی در ترسیب کربن اتمسفر و تثبیت آن اتفاق می‌افتد. همچنین غلظت پتاسیم و منیزیم در محدودۀ زیرسطح پوسته‌های زیستی بیشتر است و با افزایش عمق خاک، مقدار غلظت سدیم، کلسیم، SAR و ESP افزایش می‌یابد. لذا حفاظت از پوسته‌های زیستی و جلوگیری از تخریب آن‌ها، به‌منظور حفظ آب و خاک و همچنین هشدار در مورد تخریب فیزیکی آن‌ها و چرای بیش از حد به ساکنان و دامداران ضرورت دارد. این تحقیق می‌تواند به بهبود فهم ما از تأثیر پوسته‌های زیستی در منطقۀ تالاب آجی‌گل در استان گلستان به‌عنوان یک کانون گردوغبار در روزهای گرم و خشک کمک نماید.

https://deej.kashanu.ac.ir/article_114855_4483951e34000765e13611943c0c87d6.pdf

دانشگاه کاشان مهندسی اکوسیستم بیابان 2538-6336 13 45 2025 05 22 An Evaluation of Petroleum Mulch's Impact on Heavy Metal Concentrations in Coastal Sand Dunes of Eastern Hormozgan Province, Iran ارزیابی اثرات مالچ‌ نفتی بر غلظت فلزات سنگین ماسه‌زارهای ساحلی شرق استان هرمزگان 77 88 114857 ‎10.22052/deej.2025.256662.1101 FA محمد اکبریان گروه جغرافیا، دانشکده علوم انسانی، دانشگاه هرمزگان، بندرعباس، ایران نوازاله مرادی گروه منابع طبیعی، دانشگاه هرمزگان، بندرعباس، ایران Journal Article 2025 04 10 Introduction: Petroleum mulch is widely employed in Iran for wind erosion control, temporary sand dune stabilization, and aiding plant establishment (Jafarian, 2006; FAO, 1993). Applied as an emulsion, it forms a thin, porous layer upon dehydration, effectively stabilizing soil surfaces (Kardavani et al., 2013). However, petroleum mulch contains hydrocarbons, organic compounds, and trace amounts of heavy metals, which present potential risks of soil and groundwater contamination (McGrath, 2002; Wright, 2010; Gupta, 2016). Despite its demonstrated efficacy in reducing wind erosion and promoting vegetation growth, the heavy metal pollution resulting from its application necessitates further investigation (Azoogh et al., 2018). To ensure sustainable desertification mitigation practices, a thorough evaluation of its environmental advantages and drawbacks is crucial. Materials and Methods: This research was conducted in eastern Hormozgan Province, Iran, a region geographically bordered by the Makran Mountains to the north and the Sea of Oman to the south. Sand dune stabilization projects, employing petroleum mulch, were implemented in this area between 2002 and 2007. The study aimed to analyze heavy metal concentrations in soil samples collected from both treated areas (those subjected to petroleum mulch application and afforestation) and adjacent, untreated control areas. The elements measured included vanadium (V), nickel (Ni), tin (Sn), arsenic (As), lead (Pb), chromium (Cr), iron (Fe), aluminum (Al), cesium (Cs), manganese (Mn), cobalt (Co), and selenium (Se). Steps Involved in Research: Mapping and Sampling: Sampling points were identified within treated regions and their corresponding adjacent control areas. A crucial criterion for selection was that at least 15 years had elapsed since the completion of the stabilization projects. Treated areas were defined by the combined application of petroleum mulch and afforestation, while control areas exhibited identical geomorphological features to the treated sites prior to any intervention. Three treated-control pairs were selected for the study: Jagin, Sedich, and Biahi, located approximately 70 km, 100 km, and 150 km east of Jask, respectively. A total of 18 soil samples were collected, comprising 9 from treated areas and 9 from control areas. Laboratory Analysis: All collected soil samples were transported to the central laboratory at Hormozgan University for the measurement of heavy metal concentrations. Statistical Analysis: Data analysis was performed using SPSS, employing a two-factor factorial design to assess the individual and interactive effects of treatment (petroleum mulch application) and site conditions. Duncan's multiple range test was subsequently applied at a 5% significance level to compare the means of the various data sets. Results: Table 1 presents the variance analysis for the effects of location (representing elapsed time since application) and petroleum mulch on heavy metal concentrations in topsoil. The application of petroleum mulch significantly influenced the concentrations of lead (Pb), vanadium (V), nickel (Ni), iron (Fe), aluminum (Al), cesium (Cs), manganese (Mn), cobalt (Co), and selenium (Se) at the 5% significance level. Over time, geographical factors also had a significant effect on most elements, with the notable exceptions of chromium (Cr), arsenic (As), and manganese (Mn). Furthermore, the interaction between mulch application and location significantly impacted elements such as cesium (Cs), aluminum (Al), iron (Fe), tin (Sn), and vanadium (V), highlighting their combined effects. Figure 3 illustrates the heavy metal concentrations across the three study locations (Sedich: 2006–2007 application; Biahi: 2002 application; Jagin: 2005 application). The results indicate increased concentrations of nickel (Ni), arsenic (As), vanadium (V), chromium (Cr), tin (Sn), cesium (Cs), aluminum (Al), iron (Fe), and cobalt (Co) in Sedich. This elevation is likely attributable to the chemical composition of the petroleum mulch used. Biahi exhibited distinct concentration patterns, possibly influenced by specific local environmental conditions, while Jagin's results reflected a combination of petroleum mulch effects and other site-specific factors. Consistently, control areas maintained lower heavy metal concentrations compared to their corresponding treated areas, underscoring the role of petroleum mulch in elevating heavy metal levels (as further supported by Table 1). Figure 4 further emphasizes the significant regional differences in heavy metal concentrations. Vanadium (V), lead (Pb), aluminum (Al), and cesium (Cs) were highest in Jagin and lowest in Biahi, showing statistically significant variations among sites. In contrast, elements such as nickel (Ni), tin (Sn), chromium (Cr), iron (Fe), manganese (Mn), cobalt (Co), and selenium (Se) reached their peaks in Sedich, reinforcing the influence of regional variations. Figure 5 visualizes the average heavy metal concentrations between treated and control areas. Elements including vanadium (V), lead (Pb), nickel (Ni), chromium (Cr), aluminum (Al), arsenic (As), cobalt (Co), tin (Sn), and iron (Fe) consistently showed higher levels in treated areas. This is attributed to the inherent heavy metal content within the petroleum mulch itself. Interestingly, manganese (Mn) levels decreased in treated areas, which could be due to its absorption by plants and microorganisms thriving in the improved environmental conditions. These findings suggest that if these observed concentrations exceed established environmental standards, petroleum mulch could potentially be classified as a significant pollutant. Discussion and Conclusion: This study confirmed that petroleum mulch significantly impacts the concentrations of heavy metals in surface soil layers. Our variance analysis revealed that elements including lead (Pb), vanadium (V), nickel (Ni), iron (Fe), aluminum (Al), cesium (Cs), manganese (Mn), cobalt (Co), and selenium (Se) were significantly affected by the treatment type (control versus petroleum mulch application) at the 5% significance level. Notably, manganese concentrations decreased in areas treated with petroleum mulch, likely due to absorption by microorganisms and plants in the improved environmental conditions fostered by the mulch. Geographical location also played a significant role in the concentrations of most elements, with the exceptions of chromium (Cr), arsenic (As), and manganese (Mn). Furthermore, interaction effects between mulch application and location were observed for some elements, emphasizing the crucial role of local conditions in determining heavy metal levels in the soil. These findings are consistent with previous research. Moghadam et al. (2016) reported on the influence of mulch chemical compounds on soil heavy metal concentrations, similarly concluding that mulch compounds can alter these levels. Likewise, Gholami Tabasi et al. (2014) investigated the effects of petroleum mulches on soil contamination and found a significant increase in heavy metal concentrations due to their application. While petroleum mulch clearly contributes to elevated heavy metal concentrations in soil, it also offers substantial benefits such as stabilizing sand dunes, reducing wind erosion, improving soil structure, and accelerating soil formation (Akbarian & Nohegar, 2014). Jafari et al. (2017) observed increased richness, diversity, and uniformity in soil macrobiofauna in treated areas, without significant negative changes to vegetation cover compared to control sites. In conclusion, the application of petroleum mulch undoubtedly raises heavy metal levels in the soil due to its inherent chemical composition. However, it's crucial to note that it should only be classified as a pollutant if these concentrations exceed established environmental standards. Therefore, policymakers and decision-makers must carefully weigh both the positive environmental benefits and the potential negative impacts of petroleum mulch to ensure its sustainable use in desertification mitigation efforts. یکی از پرکاربردترین انواع مالچ در ایران، مالچ نفتی است. این ماده با افزایش پایداری سطح خاک در مقابل فرسایش بادی، فرصت مناسب برای استقرار گیاه و تثبیت دائم ماسه‌های روان را مهیا می‌کند. هدف این پژوهش، سنجش اثرات مالچ نفتی بر غلظت فلزات سنگین ماسه‌زارهای ساحلی شرق هرمزگان است. داده‌های پژوهش شامل غلظت عناصر وانادیوم، نیکل، قلع، آرسنیک، سرب، کروم، آهن، آلومینیوم، سزیم، منگنز، کبالت و سلنیوم در نمونه‌های خاک مناطق مالچ‌پاشی‌شده و محدوده‌های همجوار آن‌ها به‌عنوان شاهد است. وسایل نمونه‌برداری خاک، ادوات آزمایشگاهی فلزات سنگین، همچنین نرم‌افزارهای آماری SPSS و Excel، به‌عنوان ابزار استفاده شد. سه محدودۀ جگین، سدیچ و بیاهی، به‌ترتیب در 70، 100 و 150 کیلومتری شرق جاسک، انتخاب و از هر منطقه 6، درمجموع 18 نمونه خاک سطحی (تیمار و شاهد) برداشت شده، نمونه‌ها به آزمایشگاه منتقل و مقدار عناصر سنگین آن‌ها تعیین شد. برای تجزیه‌وتحلیل آماری، از روش تحلیل واریانس، طرح فاکتوریل دوعاملی استفاده شد. مقایسۀ میانگین داده‌ها نیز با آزمون دانکن انجام شد. براساس نتایج، مالچ نفتی تأثیر قابل توجهی بر فلزات سنگین ماسه‌زارهای ساحلی، در سطح 5 درصد معنی‌داری دارد. غلظت سرب، وانادیم، نیکل، آهن، آلومینیوم، سزیم، کبالت و سلنیم افزایش و مقدار منگنز کاهش پیدا کرده است. بر طبق نتایج، علاوه‌بر مالچ نفتی، تفاوت شرایط بین مناطق، نقش مهمی در غلظت فلزات سنگین ایفا می‌کند. درکل، اگرچه مالچ به‌دلیل وجود ترکیبات نفتی حاوی فلزات سنگین، باعث افزایش غلظت این عناصر در خاک می‌شود، در صورتی می‌توان آن ‌را یک آلایندۀ زیست‌محیطی لحاظ کرد که غلظت عناصر سنگین بالاتر از حد استاندارد افزایش یابد. نتایج این پژوهش برای تصمیم‌گیری مدیران ذی‌ربط در استفاده یا کنار گذاشتن مالچ نفتی، مفید ولی ناکافی است. لازم است سایر اثرات این ماده نظیر کاهش فرسایش بادی، بهبود شرایط فیزیکی خاک و تسریع فرایند خاک‌سازی نیز در نظر گرفته شود.

https://deej.kashanu.ac.ir/article_114857_f0021b6e03760fdd42ae5e13f702fe6c.pdf

دانشگاه کاشان مهندسی اکوسیستم بیابان 2538-6336 13 45 2025 05 22 Investigation of the Essential Oil's Phytochemical Profile in Pulicaria gnaphaloides Grown in Rangeland Ecosystems (Fars Province, Darab County, Iran) بررسی صفات فیتوشیمیایی اسانس گیاه دارویی کَک‌کُش بیابانی Pulicaria gnaphaloides (Vent.) Boiss اکوسیستم‌های مرتعی شهرستان داراب استان فارس 89 105 114860 ‎10.22052/deej.2025.256075.1089 FA علیرضا محمودی استادیار بخش مرتع و آبخیزداری، دانشکده کشاورزی و منابع طبیعی داراب، دانشگاه شیراز، ایران. کمال غلامی پورفرد استادیار بخش تولیدات گیاهی، دانشکده کشاورزی و منابع طبیعی داراب، دانشگاه شیراز، ایران 0000-0003-1702-3957 سعیده محتشمی گروه باغبانی، دانشکده کشاورزی ، دانشگاه جهرم، Journal Article 2025 01 05 Introduction: Pulicaria gnaphaloides (Vent.) Boiss, locally known as "Alaf Heizeh," is a medicinal plant prevalent in arid natural ecosystems. This species frequently colonizes disturbed and degraded lands, roadsides, dry riverbeds, and loose soils at the foothills of arid to semi-arid mountainous regions within the Irano-Turanian floristic zone. Fars Province serves as a significant habitat for P. gnaphaloides, making data collection on its distribution across the province highly valuable. Previous studies highlight the diverse medicinal properties of P. gnaphaloides, including anticancer, antioxidant, antibacterial, antiviral, disinfectant, and antifungal activities. In the traditional medicine of southern Iran, its extract is historically used as a suppository to alleviate constipation. Furthermore, topical application of an aqueous extract (decoction) from this plant acts as an insect repellent, specifically noted for its efficacy against flea bites. Given its broad traditional uses and documented biological activities, this research aims to investigate and characterize the phytochemical properties of the essential oil derived from Pulicaria gnaphaloides (Vent.) Boiss cultivated in the rangeland ecosystems of Darab County, Fars Province. Materials and Methods: Initially, the natural habitats of Pulicaria gnaphaloides (Vent.) Boiss within Darab County were identified and mapped through a field survey. For each identified area, elevation (height above sea level), latitude, and longitude were recorded using a Global Positioning System (GPS) device (Vista model, Taiwan). Given the high density and abundance of P. gnaphaloides in the Fasarood area of Darab, soil samples were collected from this specific habitat. Physical and chemical characteristics of the soil, including acidity (pH), electrical conductivity (EC), and elemental composition, were subsequently analyzed.Fresh leaves of the plant were collected from the determined habitats. To prevent degradation, the collected plant material was shade-dried at a temperature range of 10-20 degrees Celsius. For essential oil extraction, 100 grams of crushed flowering branches were subjected to hydro-distillation using a Clevenger-type apparatus for 3 hours. This process was conducted in the medicinal plants laboratory of the Faculty of Agriculture and Natural Resources, Darab. The essential oil was separated from the distillation column using a specialized syringe. The collected essential oil was then dehydrated by treating it with anhydrous sodium sulfate, weighed, and the essential oil yield percentage was calculated using a standard formula. After dehydration, the essential oil was stored in a sealed glass container at 4 degrees Celsius in a refrigerator until further analysis. The quantitative and qualitative analysis of the essential oil compounds was performed using Gas Chromatography (GC) and Gas Chromatography-Mass Spectrometry (GC-MS). Results and Discussion: The extraction and analysis of the essential oil from Pulicaria gnaphaloides (Vent.) Boiss collected from various habitats within Darab County resulted in the identification of 37 distinct chemical compounds. The essential oil yield percentage exhibited minimal variation across these habitats, averaging approximately 0.3%. The predominant chemical compounds identified in the essential oil of P. gnaphaloides across the investigated habitats were: Eudesma-4(15),7-dien-1-β-ol (20.58%), Caryophylla-4(14),8(15)-dien-5β-ol (16.68%), Terpinen-4-ol (8.40%), p-Cymene (2.48%), trans-Cadina-1(2),4-diene (2.53%), and Spathulenol (2.25%).This study reveals that the essential oil of P. gnaphaloides is a rich natural source of Eudesma and Caryophylla derivatives. This finding suggests the plant's significant potential as a valuable natural resource for pharmaceutical and related industries. The isolation and commercial extraction of compounds such as Eudesma from P. gnaphaloides essential oil could present substantial economic benefits, including potential for significant profits and foreign exchange generation for domestic stakeholders. Such endeavors could also contribute to reducing reliance on imported compounds, thereby curtailing currency outflows from the country. The identification of P. gnaphaloides as a source of these valuable compounds underscores the importance of further research into its phytochemical profile. Promoting the large-scale cultivation of this plant and optimizing essential oil extraction techniques could significantly bolster domestic industries. Moreover, the export of P. gnaphaloides essential oil or its refined derivatives could contribute substantially to strengthening Iran's economy by diversifying its export portfolio and adding value to its natural resources. گیاه کَک‌کُش بیابانی یا علف هیضه (Pulicaria gnaphaloides) یکی از گونه‌های دارویی ارزشمند است که در زیست‌بوم‌های خشک طبیعی دیده می‌شود. این گیاه بیشتر در حاشیۀ جاده‌ها و اراضی زراعی، زمین‌های دست‌خورده و آسیب‌دیده، بیابان‌های شنی، مکان‌های سنگلاخی و بستر خشک رودخانه‌های ناحیۀ رویشی ایرانی- تورانی رشد و پراکندگی دارد. در منابع گیاه‌شناسی، از آن به نام Inula gnaphalodes (مصفا، راسن و زنجبیل شامی) نیز نام برده‌اند. استان فارس یکی از رویشگاه‌های مهم این گیاه است. هدف از این پژوهش بررسی صفات فیتوشیمیایی اسانس گیاه دارویی کَک‌کُش بیابانی رشدیافته در اکوسیستم‌های مرتعی در شهرستان داراب است. برگ‌های گیاه از مناطق مختلف در رویشگاه طبیعی در مرحلۀ برگ‌دهی در سال 1402 برداشت شدند. نمونه‌ها بعد از خشک شدن در دانشکدۀ کشاورزی و منابع طبیعی داراب به کمک دستگاه کلونجر و با روش تقطیر با آب و گاز کروماتوگراف متصل‌شده به (GC) اسانس‌گیری شد. برای شناسایی ترکیبات موجود در اسانس، از دستگاه‌های پیشرفتۀ گاز کروماتوگرافی متصل به طیف‌سنج جرمی (GC/MS) بهره گرفته شد. با استخراج و آنالیز اسانس P. gnaphaloides در رویشگاه‌های مختلف، 37 ترکیب شیمیایی متفاوت شناسایی شد که بیشترین مقدار مرتبط با Eudesma-4(15),7-dien-1-b-ol (58/20 درصد)، Caryophylla-4(14),8(15)-dien-5b-ol (68/16)، Terpinen-4-ol (40/8 درصد)، p-Cymene (48/2 درصد)، trans-Cadina-1(2),4-diene (53/2 درصد) و Spathulenol (25/2 درصد) است. بر مبنای بررسی‌ها، اسانس این گیاه می‌تواند به‌عنوان یک منبع طبیعی غنی از ترکیب شیمیایی Eudesma و Caryophyllaمورد توجه شرکت‌های داروسازی قرار گیرد. استخراج ترکیب Eudesma از اسانس گیاه، می‌تواند سودآوری و ارزآوری بالای برای فعالان این حوزه در داخل داشته باشد و از خروج ارز از کشور جلوگیری کند.

https://deej.kashanu.ac.ir/article_114860_2f70b0154cbe54bd99a20e1bdd6c70d4.pdf